In a typical customer support organization (e.g., call center, service desk, electronic support center, and so forth) of a large organization (e.g., a business organization, an educational organization, or a government organization), tens to hundreds of thousands of calls may be received monthly from customers regarding various issues. Based on the calls received, the customer support organization typically attempts to identify problems that may exist in products or services. In response to these problems, the customer support organization may attempt to solve the problems, such as by improving documentation and various search tools used by technicians at the customer support organization. Additionally, the customer support organization can provide documentation on web portals to enable customers to solve problems on their own.
Conventionally, procedures and mechanisms have not been provided to efficiently and accurately identify issues that are associated with the calls received by the customer support organization. Also, procedures and mechanisms have also not been provided for efficiently and accurately quantifying received calls by a customer support organization broken down by different types of issues to enable the customer support organization to quickly determine which issues have higher priority and thus should first be addressed. Without the ability to efficiently and accurately identify and quantify issues, a customer support organization may waste resources trying to address an issue that should have lower priority than other issues.
Most calls received by customer support organizations are documented based on summaries entered by the customer call agents that received the calls. Some customer support organizations ask call agents to label each call from a menu of choices, also referred to as “issue paths.” Such labeling of calls performed by call agents is usually not accurate, since call agents are typically under time pressure to resolve a call as quickly as possible. Moreover, call agents may not be properly trained to classify a call to all the possible categories. In addition, as new categories are added, the training involved to re-train call agents to recognize the new categories can involve substantial costs. If not trained properly, call agents tend to bias classifying of calls to the top of a list, toward a catch-all “other” category, or toward overly general categories (such as a “hardware” or “software” category) without specificity. Also, if the list of categories is not complete, then the classification performed by the call agents would be incomplete. Also, the available categories in the list may not accurately describe a particular call.
Another technique of categorizing calls is based on using an expert off-line to look at information pertaining to the calls or a sample of the calls. The expert would then attempt to label the calls into various issue categories. Using an expert, or plural experts, to label calls received by a customer support organization can be time-consuming, labor-intensive, and expensive. Moreover, experts may be familiar with certain issues, while not very familiar with other issues. As a result, classification performed by such experts may be biased toward certain categories, resulting in somewhat inaccurate categorizations.
Another approach is to survey customers, in which customers are asked to fill out customer surveys or to answer questions. This process is relatively intrusive, and many customers may not be willing to participate in the survey. Moreover, the information collected from customers may be incomplete, as the customers may not be properly motivated to enter all information, or the customers may interpret different questions differently, and thus provide differing answers based on the different interpretations.
As a result of unreliable or inefficient classification of calls using conventional techniques, organizations have been unable to reliably or efficiently prioritize problems to better focus the resources of the organizations.